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Levent Guvenc

Publications and source records attributed to Levent Guvenc.

At least 19 recordsLinked to original sources
arXiv2026

Hybrid A*-Based Reverse Path-Planning of a Vehicle with Trailer System

Reverse parking maneuvering of a vehicle with trailer system is a difficult task to complete for human drivers due to the multi-body nature of the system and the unintuitive controls required to orientate the trailer properly. The problem is complicated with the presence of other vehicles that the trailer and its connected vehicle must avoid during the reverse parking maneuver. While path planning methods in reverse motion for vehicles with trailers exist, there is a lack of results that also offer collision avoidance as part of the algorithm. This paper hence proposes a modified Hybrid A*-based algorithm that can accommodate the vehicle-trailer system as well as collision avoidance considerations with the other vehicles and obstacles in the parking environment. One of the novelties of this proposed approach is its adaptability to the vehicle with trailer system, where limits of usable steering input that prevent the occurrence of jackknife incidents vary with respect to system configuration. The other contribution is the addition of the collision avoidance functionality which the standard Hybrid A* algorithm lacks. The method is developed and presented first, followed by simulation case studies to demonstrate the efficacy of the proposed approach.

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arXiv2025

High Order Control Lyapunov Function - Control Barrier Function - Quadratic Programming Based Autonomous Driving Controller for Bicyclist Safety

Ensuring the safety of Vulnerable Road Users (VRUs) is a critical challenge in the development of advanced autonomous driving systems in smart cities. Among vulnerable road users, bicyclists present unique characteristics that make their safety both critical and also manageable. Vehicles often travel at significantly higher relative speeds when interacting with bicyclists as compared to their interactions with pedestrians which makes collision avoidance system design for bicyclist safety more challenging. Yet, bicyclist movements are generally more predictable and governed by clear traffic rules as compared to the sudden and sometimes erratic pedestrian motion, offering opportunities for model-based control strategies. To address bicyclist safety in complex traffic environments, this study proposes and develops a High Order Control Lyapunov Function High Order Control Barrier Function Quadratic Programming (HOCLF HOCBF QP) control framework. Through this framework, CLFs constraints guarantee system stability so that the vehicle can track its reference trajectory, whereas CBFs constraints ensure system safety by letting vehicle avoiding potential collisions region with surrounding obstacles. Then by solving a QP problem, an optimal control command that simultaneously satisfies stability and safety requirements can be calculated. Three key bicyclist crash scenarios recorded in the Fatality Analysis Reporting System (FARS) are recreated and used to comprehensively evaluate the proposed autonomous driving bicyclist safety control strategy in a simulation study. Simulation results demonstrate that the HOCLF HOCBF QP controller can help the vehicle perform robust, and collision-free maneuvers, highlighting its potential for improving bicyclist safety in complex traffic environments.

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arXiv2025

Modified Hybrid A* Collision-Free Path-Planning for Automated Reverse Parking

Parking a vehicle in tight spaces is a challenging task to perform due to the scarcity of feasible paths that are also collision-free. This paper presents a strategy to tackle this kind of maneuver with a modified Hybrid-A* path-planning algorithm that combines the feasibility guarantee inherent in the standard Hybrid A* algorithm with the addition of static obstacle collision avoidance. A kinematic single-track model is derived to describe the low-speed motion of the vehicle, which is subsequently used as the motion model in the Hybrid A* path-planning algorithm to generate feasible motion primitive branches. The model states are also used to reconstruct the vehicle centerline, which, in conjunction with an inflated binary occupancy map, facilitates static obstacle collision avoidance functions. Simulation study and animation are set up to test the efficacy of the approach, and the proposed algorithm proves to consistently provide kinematically feasible trajectories that are also collision-free.

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arXiv2025

Delay Tolerant Control for Autonomous Driving Using CDOB

With the rapid growth of autonomous vehicle technologies, effective path-tracking control has become a critical component in ensuring safety and efficiency in complex traffic scenarios. When a high level decision making agent generates a collision free path, a robust low level controller is required to precisely follow this trajectory. However, connected autonomous vehicles (CAV) are inherently affected by communication delays and computation delays, which significantly degrade the performance of conventional controllers such as PID or other more advanced controllers like disturbance observers (DOB). While DOB-based designs have shown effectiveness in rejecting disturbances under nominal conditions, their performance deteriorates considerably in the presence of unknown time delays. To address this challenge, this paper proposes a delay-tolerant communication disturbance observer (CDOB) framework for path-tracking control in delayed systems. The proposed CDOB compensates for the adverse effects of time delays, maintaining accurate trajectory tracking even under uncertain and varying delay conditions. It is shown through a simulation study that the proposed control architecture maintains close alignment with the reference trajectory across various scenarios, including single lane change, double-= lane change, and Elastic Band generated collision avoidance paths under various time delays. Simulation results further demonstrate that the proposed method outperforms conventional approaches in both tracking accuracy and delay robustness, making it well suited for autonomous driving applications.

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arXiv2025

Nonlinear Model Predictive Control-Based Reverse Path-Planning and Path-Tracking Control of a Vehicle with Trailer System

Reverse parking maneuvers of a vehicle with trailer system is a challenging task to complete for human drivers due to the unstable nature of the system and unintuitive controls required to orientate the trailer properly. This paper hence proposes an optimization-based automation routine to handle the path-planning and path-tracking control process of such type of maneuvers. The proposed approach utilizes nonlinear model predictive control (NMPC) to robustly guide the vehicle-trailer system into the desired parking space, and an optional forward repositioning maneuver can be added as an additional stage of the parking process to obtain better system configurations, before backward motion can be attempted again to get a good final pose. The novelty of the proposed approach is the simplicity of its formulation, as the path-planning and path-tracking operations are only conducted on the trailer being viewed as a standalone vehicle, before the control inputs are propagated to the tractor vehicle via inverse kinematic relationships also derived in this paper. Simulation case studies and hardware-in-the-loop tests are performed, and the results demonstrate the efficacy of the proposed approach.

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arXiv2025

Vehicle-in-Virtual-Environment (VVE) Method for Developing and Evaluating VRU Safety of Connected and Autonomous Driving with Focus on Bicyclist Safety

Extensive research has already been conducted in the autonomous driving field to help vehicles navigate safely and efficiently. At the same time, plenty of current research on vulnerable road user (VRU) safety is performed which largely concentrates on perception, localization, or trajectory prediction of VRUs. However, existing research still exhibits several gaps, including the lack of a unified planning and collision avoidance system for autonomous vehicles, limited investigation into delay tolerant control strategies, and the absence of an efficient and standardized testing methodology. Ensuring VRU safety remains one of the most pressing challenges in autonomous driving, particularly in dynamic and unpredictable environments. In this two year project, we focused on applying the Vehicle in Virtual Environment (VVE) method to develop, evaluate, and demonstrate safety functions for Vulnerable Road Users (VRUs) using automated steering and braking of ADS. In this current second year project report, our primary focus was on enhancing the previous year results while also considering bicyclist safety.

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arXiv2025

Vehicle-in-Virtual-Environment (VVE) Based Autonomous Driving Function Development and Evaluation Methodology for Vulnerable Road User Safety

Traditional methods for developing and evaluating autonomous driving functions, such as model-in-the-loop (MIL) and hardware-in-the-loop (HIL) simulations, heavily depend on the accuracy of simulated vehicle models and human factors, especially for vulnerable road user safety systems. Continuation of development during public road deployment forces other road users including vulnerable ones to involuntarily participate in the development process, leading to safety risks, inefficiencies, and a decline in public trust. To address these deficiencies, the Vehicle-in-Virtual-Environment (VVE) method was proposed as a safer, more efficient, and cost-effective solution for developing and testing connected and autonomous driving technologies by operating the real vehicle and multiple other actors like vulnerable road users in different test areas while being immersed within the same highly realistic virtual environment. This VVE approach synchronizes real-world vehicle and vulnerable road user motion within the same virtual scenario, enabling the safe and realistic testing of various traffic situations in a safe and repeatable manner. In this paper, we propose a new testing pipeline that sequentially integrates MIL, HIL, and VVE methods to comprehensively develop and evaluate autonomous driving functions. The effectiveness of this testing pipeline will be demonstrated using an autonomous driving path-tracking algorithm with local deep reinforcement learning modification for vulnerable road user collision avoidance.

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arXiv2025

Development of an Advisory System for Parking of a Car and Trailer

Trailer parking is a challenging task due to the unstable nature of the vehicle-trailer system in reverse motion and the unintuitive steering actions required at the vehicle to accomplish the parking maneuver. This paper presents a strategy to tackle this kind of maneuver with an advisory graphic aid to help the human driver with the task of manually backing up the vehicle-trailer system. A kinematic vehicle-trailer model is derived to describe the low-speed motion of the vehicle-trailer system, and its inverse kinematics is established by generating an equivalent virtual trailer axle steering command. The advisory system graphics is generated based on the inverse kinematics and displays the expected trailer orientation given the current vehicle steer angle and configuration (hitch angle). Simulation study and animation are set up to test the efficacy of the approach, where the user can select both vehicle speed and vehicle steering angle freely, which allows the user to stop the vehicle-trailer system and experiment with different steering inputs to see their effect on the predicted trailer motion before proceeding with the best one according to the advisory graphics, hence creating a series of piecewise continuous control actions similar to how manual trailer reverse parking is usually carried out. The advisory graphics proves to provide the driver with an intuitive understanding of the trailer motion at any given configuration (hitch angle).

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arXiv2024

Path Planning and Robust Path Tracking Control of an Automated Parallel Parking Maneuver

Self driving vehicles should be able to perform parallel parking or a similar maneuver successfully. With this motivation, the S shaped maneuverability test of the Ohio driver license examination is chosen here for automatic execution by a self driving vehicle with drive by wire capability and longitudinal and lateral controls. The Ohio maneuverability test requires the driver to start within an area enclosed by four pylons and the driver is asked to go to the left of the fifth pylon directly in front of the vehicle in a smooth and continuous manner while ending in a parallel direction to the initial one. The driver is then asked to go backwards to the starting location of the vehicle without stopping the vehicle or hitting the pylons. As a self driving vehicle should do a much better job repeatably than a driver, a high order polynomial path model is built along with speed profiling to start and stop smoothly at the ends of the path without large longitudinal and lateral accelerations. In contrast to the long horizon, higher speed path planning and path tracking control applications in the literature, this paper treats low speed and very short horizon path planning and path tracking control with stopping and direction reversal. The path is constructed using a segmented polynomial fit optimization routine that guarantees path curvature smoothness. A linear path tracking model is utilized as the basis of the designed control system consisting of a disturbance observer based curvature rejection filter and a speed scheduled, parameter space robust PID controller. Simulation studies indicate that it has better performance compared to other common control systems such as standalone PID controller and combined PID and feedforward control. indicate that it has better performance compared to other common control systems such as standalone PID controller and combined PID and feedforward control.

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arXiv2024

Vehicle-in-Virtual-Environment Method for ADAS and Connected and Automated Driving Function Development/Demonstration/Evaluation

The current approach for new Advanced Driver Assistance System (ADAS) and Connected and Automated Driving (CAD) function development involves a significant amount of public road testing which is inefficient due to the number miles that need to be driven for rare and extreme events to take place, thereby being very costly also, and unsafe as the rest of the road users become involuntary test subjects. A new development, evaluation and demonstration method for safe, efficient, and repeatable development, demonstration and evaluation of ADAS and CAD functions called VehicleInVirtualEnvironment (VVE) was recently introduced as a solution to this problem. The vehicle is operated in a large, empty, and flat area during VVE while its localization and perception sensor data is fed from the virtual environment with other traffic and rare and extreme events being generated as needed. The virtual environment can be easily configured and modified to construct different testing scenarios on demand. This paper focuses on the VVE approach and introduces the coordinate transformations needed to sync pose (location and orientation) in the virtual and physical worlds and handling of localization and perception sensor data using the highly realistic 3D simulation model of a recent autonomous shuttle deployment site in Columbus, Ohio as the virtual world. As a further example that uses multiple actors, the use of VVE for VehicleToVRU communication based Vulnerable Road User (VRU) safety is presented in the paper using VVE experiments and real pedestrian(s) in a safe and repeatable manner. VVE experiments are used to demonstrate the efficacy of the method.

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arXiv2023

Cooperative Adaptive Cruise Control Design and Implementation

In this manuscript a design and implementation of CACC on an autonomous vehicle platform (2017 Ford Fusion) is presented. The developed CACC controls the intervehicle distance between the target vehicle and ego vehicle using a feedforward PD controller. In this design the feedforward information is the acceleration of the target vehicle which is communicated through Dedicated Short-Range Communication (DSRC) modem. The manuscript explains the detailed architecture of the designed CACC with used hardware and methods for the both simulation and experiments. Also, an approach to overcome detection failures at the curved roads is presented to improve overall quality of the designed CACC system. As a result, the initial simulation and experimental results with the designed CACC system is presented in the paper. The presented results indicate that CACC improves the car following performance of the ego vehicle as compared to the classical Adaptive Cruise Controller.

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arXiv2023

Use of Robust DOB/CDOB Compensation to Improve Autonomous Vehicle Path Following Performance in the Presence of Model Uncertainty, CAN Bus Delays and External Disturbances

A path tracking control system is chosen as the proof-of-concept demonstration application in this paper. A disturbance observer (DOB) is embedded within the steering to path error automated driving loop to handle uncertain parameters such as vehicle mass, vehicle velocities and road friction coefficient and to reject yaw moment disturbances. The compensation of vehicle model with the embedded disturbance observer forces it to behave like its nominal model within the bandwidth of the disturbance observer. A parameter space approach based steering controller is then used to optimize performance. The proposed method demonstrates good disturbance rejection and achieves stability robustness. The variable time delay from the steer-by-wire system in an actual vehicle can also lead to stability issues since it adds large negative phase angle to the plant frequency response and tends to destabilize it. A communication disturbance observer (CDOB) based time delay compensation approach that does not require exact knowledge of this time delay is embedded into the steering actuation loop to handle this problem. Stability analysis of both DOB and CDOB compensation system are presented in this paper. Extensive model-in-the-loop simulations were performed to test the designed disturbance observer and CDOB systems and show reduced path following errors in the presence of uncertainty, disturbances and time delay. A validated model of our 2017 Ford Fusion Hybrid research autonomous vehicle is used in the simulation analyses. Simulation results verify the performance enhancement of the vehicle path following control with proposed DOB and CDOB structure. A HiL simulator that uses a validated CarSim model with sensors and traffic will be used later to verify the real time capability of our approach.

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arXiv2023

Impact of Different Desired Velocity Profiles and Controller Gains on Convoy Driveability of Cooperative Adaptive Cruise Control Operated Platoons

As the development of autonomous vehicles rapidly advances, the use of convoying/platooning becomes a more widely explored technology option for saving fuel and increasing the efficiency of traffic. In cooperative adaptive cruise control (CACC), the vehicles in a convoy follow each other under adaptive cruise control (ACC) that is augmented by the sharing of preceding vehicle acceleration through the vehicle to vehicle communication in a feedforward control path. In general, the desired velocity optimization for vehicles in the convoy is based on fuel economy optimization, rather than driveability. This paper is a preliminary study on the impact of the desired velocity profile on the driveability characteristics of a convoy of vehicles and the controller gain impact on the driveability. A simple low-level longitudinal model of the vehicle has been used along with a PD type cruise controller and a generic spacing policy for ACC/CACC. The acceleration of the previous vehicle is available to the next vehicle as input, and the simulations are performed as Cooperative Adaptive Cruise Control of a convoy of vehicles. Individual vehicle acceleration profiles have been analyzed for driveability for two different velocity profiles that are followed in a stretch of 720 m between stop signs. The controller gains have been re-tuned based on the parameter space robust control PID approach for driveability and compared with the original gains. The US06 SFTP drive cycle has also been used for the comparison of the two different controller gain sets.

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arXiv2023

Hardware-in-the-Loop and Road Testing of RLVW and GLOSA Connected Vehicle Applications

This paper presents an evaluation of two different Vehicle to Infrastructure (V2I) applications, namely Red Light Violation Warning (RLVW) and Green Light Optimized Speed Advisory (GLOSA). The evaluation method is to first develop and use Hardware-in-the-Loop (HIL) simulator testing, followed by extension of the HIL testing to road testing using an experimental connected vehicle. The HIL simulator used in the testing is a state-of-the-art simulator that consists of the same hardware like the road side unit and traffic cabinet as is used in real intersections and allows testing of numerous different traffic and intersection geometry and timing scenarios realistically. First, the RLVW V2I algorithm is tested in the HIL simulator and then implemented in an On-Board-Unit (OBU) in our experimental vehicle and tested at real world intersections. This same approach of HIL testing followed by testing in real intersections using our experimental vehicle is later extended to the GLOSA application. The GLOSA application that is tested in this paper has both an optimal speed advisory for passing at the green light and also includes a red light violation warning system. The paper presents the HIL and experimental vehicle evaluation systems, information about RLVW and GLOSA and HIL simulation and road testing results and their interpretations.

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arXiv2023

Cooperative Collision Avoidance in a Connected Vehicle Environment

Connected vehicle (CV) technology is among the most heavily researched areas in both the academia and industry. The vehicle to vehicle (V2V), vehicle to infrastructure (V2I) and vehicle to pedestrian (V2P) communication capabilities enable critical situational awareness. In some cases, these vehicle communication safety capabilities can overcome the shortcomings of other sensor safety capabilities because of external conditions such as 'No Line of Sight' (NLOS) or very harsh weather conditions. Connected vehicles will help cities and states reduce traffic congestion, improve fuel efficiency and improve the safety of the vehicles and pedestrians. On the road, cars will be able to communicate with one another, automatically transmitting data such as speed, position, and direction, and send alerts to each other if a crash seems imminent. The main focus of this paper is the implementation of Cooperative Collision Avoidance (CCA) for connected vehicles. It leverages the Vehicle to Everything (V2X) communication technology to create a real-time implementable collision avoidance algorithm along with decision-making for a vehicle that communicates with other vehicles. Four distinct collision risk environments are simulated on a cost effective Connected Autonomous Vehicle (CAV) Hardware in the Loop (HIL) simulator to test the overall algorithm in real-time with real electronic control and communication hardware.

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arXiv2023

Discrete-time Robust PD Controlled System with DOB/CDOB Compensation for High Speed Autonomous Vehicle Path Following

Autonomous vehicle path following performance is one of significant consideration. This paper presents discrete time design of robust PD controlled system with disturbance observer (DOB) and communication disturbance observer (CDOB) compensation to enhance autonomous vehicle path following performance. Although always implemented on digital devices, DOB and CDOB structure are usually designed in continuous time in the literature and also in our previous work. However, it requires high sampling rate for continuous-time design block diagram to automatically convert to corresponding discrete-time controller using rapid controller prototyping systems. In this paper, direct discrete time design is carried out. Digital PD feedback controller is designed based on the nominal plant using the proposed parameter space approach. Zero order hold method is applied to discretize the nominal plant, DOB and CDOB structure in continuous domain. Discrete time DOB is embedded into the steering to path following error loop for model regulation in the presence of uncertainty in vehicle parameters such as vehicle mass, vehicle speed and road-tire friction coefficient and rejecting external disturbance like crosswind force. On the other hand, time delay from CAN bus based sensor and actuator command interfaces results in degradation of system performance since large negative phase angles are added to the plant frequency response. Discrete time CDOB compensated control system can be used for time delay compensation where the accurate knowledge of delay time value is not necessary. A validated model of our lab Ford Fusion hybrid automated driving research vehicle is used for the simulation analysis while the vehicle is driving at high speed. Simulation results successfully demonstrate the improvement of autonomous vehicle path following performance with the proposed discrete time DOB and CDOB structure.

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arXiv2023

The Effects of Varying Penetration Rates of L4-L5 Autonomous Vehicles on Fuel Efficiency and Mobility of Traffic Networks

Microscopic traffic simulators that simulate realistic traffic flow are crucial in studying, understanding and evaluating the fuel usage and mobility effects of having a higher number of autonomous vehicles (AVs) in traffic under realistic mixed traffic conditions including both autonomous and non-autonomous vehicles. In this paper, L4-L5 AVs with varying penetration rates in total traffic flow were simulated using the microscopic traffic simulator Vissim on urban, mixed and freeway roadways. The roadways used in these simulations were replicas of real roadways in and around Columbus, Ohio, including an AV shuttle routes in operation. The road-specific information regarding each roadway, such as the number of traffic lights and positions, number of STOP signs and positions, and speed limits, were gathered using OpenStreetMap with SUMO. In simulating L4-L5 AVs, the All-Knowing CoEXist AV and a vehicle with Wiedemann 74 driver were taken to represent AV and non-AV driving, respectively. Then, the driving behaviors, such as headway time and car following, desired acceleration and deceleration profiles of AV, and non-AV car following and lane change models were modified. The effect of having varying penetration rates of L4-L5 AVs were then evaluated using criteria such as average fuel consumption, existence of queues and their average/maximum length, total number of vehicles in the simulation, average delay experience by all vehicles, total number of stops experienced by all vehicles, and total emission of CO, NOx and volatile organic compounds (VOC) from the vehicles in the simulation. The results show that while increasing penetration rates of L4-L5 AVs generally improve overall fuel efficiency and mobility of the traffic network, there were also cases when the opposite trend was observed.

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arXiv2023

Pre-Deployment Testing of Low Speed, Urban Road Autonomous Driving in a Simulated Environment

Low speed autonomous shuttles emulating SAE Level L4 automated driving using human driver assisted autonomy have been operating in geo-fenced areas in several cities in the US and the rest of the world. These autonomous vehicles (AV) are operated by small to mid-sized technology companies that do not have the resources of automotive OEMs for carrying out exhaustive, comprehensive testing of their AV technology solutions before public road deployment. Due to the low speed of operation and hence not operating on roads containing highways, the base vehicles of these AV shuttles are not required to go through rigorous certification tests. The way the driver assisted AV technology is tested and allowed for public road deployment is continuously evolving but is not standardized and shows differences between the different states where these vehicles operate. Currently, AVs and AV shuttles deployed on public roads are using these deployments for testing and improving their technology. However, this is not the right approach. Safe and extensive testing in a lab and controlled test environment including Model-in-the-Loop (MiL), Hardware-in-the-Loop (HiL) and Autonomous-Vehicle-in-the-Loop (AViL) testing should be the prerequisite to such public road deployments. This paper presents three dimensional virtual modeling of an AV shuttle deployment site and simulation testing in this virtual environment. We have two deployment sites in Columbus of these AV shuttles through the Department of Transportation funded Smart City Challenge project named Smart Columbus. The Linden residential area AV shuttle deployment site of Smart Columbus is used as the specific example for illustrating the AV testing method proposed in this paper.

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